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Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

dc.rights.licensehttp://creativecommons.org/licenses/by/4.0es_MX
dc.creatorOMAR YAXMEHEN BELLO CHAVOLLAes_MX
dc.creatorJESSICA PAOLA BAHENA LOPEZes_MX
dc.creatorARSENIO VARGAS VAZQUEZes_MX
dc.creatorNeftali Eduardo Antonio Villaes_MX
dc.creatorAlejandro Márquez Salinases_MX
dc.creatorCarlos Alberto Fermín Martínezes_MX
dc.creatorMARIA ROSALBA ROJAS MARTINEZes_MX
dc.creatorROOPA PRAVIN MEHTAes_MX
dc.creatorIVETTE CRUZ BAUTISTAes_MX
dc.creatorMIGUEL SERGIO HERNANDEZ JIMENEZes_MX
dc.creatorANA CRISTINA GARCIA ULLOAes_MX
dc.creatorPALOMA ALMEDA VALDESes_MX
dc.creatorCARLOS ALBERTO AGUILAR SALINASes_MX
dc.date2020
dc.date.accessioned2021-11-17T19:22:59Z
dc.date.available2021-11-17T19:22:59Z
dc.identifier.urihttp://repositorio.inger.gob.mx/jspui/handle/20.500.12100/17308
dc.descriptionIntroduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings. Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup. Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89). Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.es_MX
dc.formatAdobe PDFes_MX
dc.languageenges_MX
dc.publisherBMJ Publishing Groupes_MX
dc.relationhttps://drc.bmj.com/content/8/1/e001550.longes_MX
dc.relation.requiresSies_MX
dc.rightsAcceso Abiertoes_MX
dc.sourceBMJ Open Diabetes Research and Care (2052-4897) Vol. 8 (2020)es_MX
dc.subjectMEDICINA Y CIENCIAS DE LA SALUDes_MX
dc.subjectCiencias médicases_MX
dc.subjectCiencias clínicases_MX
dc.subjectGeriatríaes_MX
dc.subjectDiabeteses_MX
dc.subjectDiabetes mellituses_MX
dc.subjectObesidades_MX
dc.subjectObesityes_MX
dc.subjectEncuesta de Salud Nacional y Examen de Nutriciónes_MX
dc.subjectNational Health and Nutrition Examination Surveyes_MX
dc.subjectMéxicoes_MX
dc.subjectMexicoes_MX
dc.titleClinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approaches_MX
dc.typeArtículoes_MX
dc.audienceResearcherses_MX
dc.creator.idBECO920925HDFLHM05es_MX
dc.creator.idBALJ930827MDFHPS02es_MX
dc.creator.idVAVA940920HGRRZR01es_MX
dc.creator.idCA1343917es_MX
dc.creator.idCA1343931es_MX
dc.creator.idCA1343932es_MX
dc.creator.idROMR600708MPLJRS08es_MX
dc.creator.idMEXR720429MNEHXP03es_MX
dc.creator.idCUBI740301MDFRTV06es_MX
dc.creator.idHEJM631007HDFRMG01es_MX
dc.creator.idGAUA801214MDFRLN04es_MX
dc.creator.idAEVP760218MDFLLL01es_MX
dc.creator.idAUSC611116HDFGLR00es_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
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dc.creator.nameIdentifiercaes_MX
dc.creator.nameIdentifiercaes_MX
dc.creator.nameIdentifiercaes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX
dc.creator.nameIdentifiercurpes_MX


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